Becerra-Sánchez Aldonso, Rodarte-Rodríguez Armando, Escalante-García Nivia I, Olvera-González José E, De la Rosa-Vargas José I, Zepeda-Valles Gustavo, Velásquez-Martínez Emmanuel de J
Unidad Académica de Ingenieía Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico.
Laboratorio de Iluminación Artificial, Tecnológico Nacional de México Campus Pabellón de Arteaga, Aguascalientes 20670, Mexico.
Diagnostics (Basel). 2022 Jun 5;12(6):1396. doi: 10.3390/diagnostics12061396.
The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.
由新冠病毒引发的这场新的大流行已导致全球各临床中心的医疗质量不堪重负。造成这一情况的原因包括医护人员短缺、基础设施不足、药品短缺等。新冠病毒感染患者数量的迅速且呈指数级增长，需要对可能的感染情况及其后果进行高效且快速的预测，以减轻医疗质量负担。因此，人们开发并应用智能模型来辅助医护人员，使他们能够对新冠病毒感染患者的健康状况做出更有效的诊断。本文旨在提出一种用于预测墨西哥新冠病毒感染患者健康状况的替代性算法分析方法。对不同的预测模型，如KNN、逻辑回归、随机森林、人工神经网络和多数投票法进行了评估和比较。这些模型将风险因素作为变量来预测新冠病毒感染患者的死亡率。最成功的方案是所提出的基于人工神经网络的模型，其准确率达到90%，F1分数为89.64%。数据分析表明，肺炎、高龄和插管需求是对墨西哥因该病毒导致死亡影响最大的风险因素。
